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Journal ArticleDOI

Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA

25 May 2019-Concurrency and Computation: Practice and Experience (John Wiley & Sons, Ltd)-Vol. 31, Iss: 10
TL;DR: A novel correlated extended Kalman filter (CEKF) is proposed, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, and the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix.
Abstract: It is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.
Citations
More filters
01 Jan 2016
TL;DR: In this paper, a decentralized particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated is presented, where each estimation module is independent from others and only uses local measurements.
Abstract: This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.

15 citations

Journal ArticleDOI
28 Aug 2021-Energies
TL;DR: This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation and provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.
Abstract: Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

9 citations

References
More filters
BookDOI
24 Mar 2004
TL;DR: In this paper, Peters and Wilkinson this paper proposed a WLS state estimation algorithm based on the Nodal Variable Formulation (NVF) and the Branch Variable Factorization (BVF).
Abstract: Preface INTRODUCTION Operating States of a Power System Power System Security Analysis State Estimation Summary WEIGHTED LEAST SQUARES STATE ESTIMATION Introduction Component Modeling and Assumptions Building the Network Model Maximum Likelihood Estimation Measurement Model and Assumptions WLS State Estimation Algorithm Decoupled Formulation of the WLS State Estimation DC State Estimation Model Problems References ALTERNATIVE FORMULATIONS OF THE WLS STATE ESTIMATION Weaknesses of the Normal Equations Formulation Orthogonal Factorization Hybrid Method Method of Peters and Wilkinson Equality-Constrained WLS State Estimation Augmented Matrix Approach Blocked Formulation Comparison of Techniques Problems References NETWORK OBSERVABILITY ANALYSIS Networks and Graphs NetworkMatrices LoopEquations Methods of Observability Analysis Numerical Method Based on the Branch Variable Formulation Numerical Method Based on the Nodal Variable Formulation Topological Observability Analysis Method Determination of Critical Measurements Measurement Design Summary Problems References BAD DATA DETECTION AND IDENTIFICATION Properties of Measurement Residuals Classification of Measurements Bad Data Detection and IdentiRability Bad Data Detection Properties of Normalized Residuals Bad Data Identification Largest Normalized Residual Test Hypothesis Testing Identification (HTI) Summary Problems References ROBUST STATE ESTIMATION Introduction Robustness and Breakdown Points Outliers and Leverage Points M-Estimators Least Absolute Value (LAV) Estimation Discussion Problems References NETWORK PARAMETER ESTIMATION Introduction Influence of Parameter Errors on State Estimation Results Identification of Suspicious Parameters Classification of Parameter Estimation Methods Parameter Estimation Based on Residua! Sensitivity Analysis Parameter Estimation Based on State Vector Augmentation Parameter Estimation Based on Historical Series of Data Transformer Tap Estimation Observability of Network Parameters Discussion Problems References TOPOLOGY ERROR PROCESSING Introduction Types of Topology Errors Detection of Topology Errors Classification of Methods for Topology Error Analysis Preliminary Topology Validation Branch Status Errors Substation Configuration Errors Substation Graph and Reduced Model Implicit Substation Model: State and Status Estimation Observability Analysis Revisited Problems References STATE ESTIMATION USING AMPERE MEASUREMENTS Introduction Modeling of Ampere Measurements Difficulties in Using Ampere Measurements Inequality-Constrained State Estimation Heuristic Determination of F-# Solution Uniqueness Algorithmic Determination of Solution Uniqueness Identification of Nonuniquely Observable Branches Measurement Classification and Bad Data Identification Problems References Appendix A Review of Basic Statistics Appendix B Review of Sparse Linear Equation Solution References Index

2,639 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications, including the unscented Kalman Filter, the particle filter, and the extended Kalman Filtering.
Abstract: The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

836 citations

Journal ArticleDOI
TL;DR: The fault detection filtering problem is solved for nonlinear switched stochastic system in the T-S fuzzy framework and the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted H∞ error performance.
Abstract: In this note, the fault detection filtering problem is solved for nonlinear switched stochastic system in the T-S fuzzy framework. Our attention is concentrated on the construction of a robust fault detection technique to the nonlinear switched system with Brownian motion. Based on observer-based fault detection fuzzy filter as a residual generator, the proposed fault detection is formulated as a fuzzy filtering problem. By the utilization of the average dwell time technique and the piecewise Lyapunov function technique, the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted ${\mathcal H}_{\infty}$ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is also established by the linearization procedure technique. Finally, simulation has been presented to show the effectiveness of the proposed fault detection technique.

452 citations

Journal ArticleDOI
TL;DR: The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links.
Abstract: This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.

434 citations

Journal ArticleDOI
TL;DR: In this paper, an extended Kalman filter (EKF) technique for dynamic state estimation of a synchronous machine using phasor measurement unit (PMU) quantities is developed.
Abstract: Availability of the synchronous machine angle and speed variables give us an accurate picture of the overall condition of power networks leading therefore to an improved situational awareness by system operators. In addition, they would be essential in developing local and global control schemes aimed at enhancing system stability and reliability. In this paper, the extended Kalman filter (EKF) technique for dynamic state estimation of a synchronous machine using phasor measurement unit (PMU) quantities is developed. The simulation results of the EKF approach show the accuracy of the resulting state estimates. However, the traditional EKF method requires that all externally observed variables, including input signals, be measured or available, which may not always be the case. In synchronous machines, for example, the exciter output voltage Efd may not be available for measuring in all cases. As a result, the extended Kalman filter with unknown inputs, referred to as EKF-UI, is proposed for identifying and estimating the states and the unknown inputs of the synchronous machine simultaneously. Simulation results demonstrate the efficiency and accuracy of the EKF-UI method under noisy or fault conditions, compared to the classic EKF approach and confirms its great potential in cases where there is no access to the input signals of the system.

420 citations